Estimating distribution and abundance of wide‐ranging species with integrated spatial models: Opportunities revealed by the first wolf assessment in south‐central Italy

Abstract Estimating demographic parameters for wide‐ranging and elusive species living at low density is challenging, especially at the scale of an entire country. To produce wolf distribution and abundance estimates for the whole south‐central portion of the Italian wolf population, we developed an integrated spatial model, based on the data collected during a 7‐month sampling campaign in 2020–2021. Data collection comprised an extensive survey of wolf presence signs, and an intensive survey in 13 sampling areas, aimed at collecting non‐invasive genetic samples (NGS). The model comprised (i) a single‐season, multiple data‐source, multi‐event occupancy model and (ii) a spatially explicit capture‐recapture model. The information about species' absence was used to inform local density estimates. We also performed a simulation‐based assessment, to estimate the best conditions for optimizing sub‐sampling and population modelling in the future. The integrated spatial model estimated that 74.2% of the study area in south‐central Italy (95% CIs = 70.5% to 77.9%) was occupied by wolves, for a total extent of the wolf distribution of 108,534 km2 (95% CIs = 103,200 to 114,000). The estimate of total population size for the Apennine wolf population was of 2557 individuals (SD = 171.5; 95% CIs = 2127 to 2844). Simulations suggested that the integrated spatial model was associated with an average tendency to slightly underestimate population size. Also, the main contribution of the integrated approach was to increase precision in the abundance estimates, whereas it did not affect accuracy significantly. In the future, the area subject to NGS should be increased to at least 30%, while at least a similar proportion should be sampled for presence‐absence data, to further improve the accuracy of population size estimates and avoid the risk of underestimation. This approach could be applied to other wide‐ranging species and in other geographical areas, but specific a priori evaluations of model requirements and expected performance should be made.


Table of Contents:
Appendix 1 -Description of laboratory methods with details on primers and amplification profiles for all the genotyped markers Page 2 Table S1 -Description of the genotyped autosomal (CFA) and Ylinked (CFAY) microsatellites (STR), Amelogenin and β-defensin CBD103 (K-locus) genes, and the hypervariable part of the mtDNA control-region (mtDNA CR1).M1-6: progressive numbers of the multiplexed amplification reactions.
Page 8 Table S2 -Description of the genetic variability obtained analysing the 12-STR genotypes identified from both non-invasively and invasively collected samples.
extension (25 cycles).PCR products were analysed in an ABI 3130XL automated sequencer using the ABI programs SEQUENCINGANALYSIS v. 3.7 and SEQSCAPE v. 2.5.Detected haplotypes were compared with sequences available from GenBank using Blast (Altschul et al., 1990).

Description of the multiple-tube protocol
The genotypes were identified using a multiple-tube procedure consisting of the following steps.
Consensus genotypes were reconstructed from the 4-8 replicated amplifications per locus per sample foreseen by the multi-tube approach using the software GIMLET v.1.3.3 (Valière 2002), accepting the heterozygotes only if both alleles were seen in at least two replicates, and the homozygotes only if a single allele was observed in at least four replicates.
GIMLET was also used to match the reconstructed genotypes to each other and with the ISPRA Canis database (Caniglia et al. 2020) to identify identical genotypes and individual recaptures.
The consensus genotypes and the 4-8 replicated amplifications per locus per sample needed to obtain them were finally used in GIMLET to compute the amplification success (PCR+ = the number of successful PCRs divided by the total number of PCR runs across samples), and estimate the allelic dropout (ADO = the number of allelic dropouts over the number of successful amplifications of heterozygous genotypes at a given locus) and false allele (FA = the number of amplifications leading to one or more false alleles at a locus over the total number of successful amplifications at that locus) rates (Caniglia et al. 2014).

Taxon identification
The 12-STR multilocus genotypes were assigned to their taxon of origin (wolf, dog or admixed  et al. 2020).We ran five repetitions of PARALLELSTRUCTURE with 5×10 5 iterations following a burn-in period of 5×10 4 iterations using the Admixture (A) and Independent allele frequencies (F) models (Falush et al. 2003), and assuming K=2 a priori clusters (corresponding to the optimal number of genetic clusters in which reference populations are split).We used the software CLUMPAK (Kopelman et al. 2015) to concatenate the data from the five independent runs for each K.
Based on their assignment membership proportions to the reference wolf population (qw) and the information derived from the uniparental and functional markers (four Y-linked STRs, K-locus), we classified the unknown individual genotypes as: "wolf" if qw ≥ 0.990 and no domestic component at the other analysed markers; "recent hybrid" if qw ˂ 0.975; "introgressed wolf" if 0.975 ≤ qw < 0.990 and/or if they showed a domestic Y-haplotype, as well as the presence of the melanistic deletion at the β-defensin (Caniglia et al. 2020).

Sample genotyping and Taxon identification
After the four to eight replicated PCR per sample per locus foreseen by the multiple-tube protocol, 971 (61%) of the 1600 non-invasively collected samples were reliable genotyped (R ≥ 0.990) at the 12 autosomal STRs, showing an average number of positive amplifications per locus of 0.90 (ranging from 0.82 to 0.96), and average among loci rates of ADO = 0.19 (± 0.04 SD) and FA = 0.0008 (± 0.0010).Regrouping procedures identified them as belonging to 590 individual genotypes (235 females, 312 males and 43 with undetermined gender).All the 32 biological samples obtained from found dead or live-trapped animals produced reliable new genotypes (R ≥ 0.990) never previously sampled (23 females and 9 males), showing no evidence of ADO or FA errors.
Finally, joining the genotypes detected from the invasively and non-invasively collected samples, a total of 622 individual genotypes were identified and definitely accepted (R ≥ 0.990): 258 (41%) females, 321 (52%) males and 43 (7%) with undetermined gender.These 622 detected genotypes showed multilocus PID = 5.5 x 10 -11 and PIDsibs = 6.4 x 10 -5 , meaning that only 6.4 individuals in 100,000 siblings are expected to share by chance an identical genotype, suggesting no ''shadow effect'' (all the detected genotypes can be considered as distinct individuals; Mills et al. 2000), and that matching genotypes can be considered as recaptures of the same individual.Resampling frequencies were heterogeneous: 423 genotypes (68%) were sampled only once, while the other 199 (31.9%) were sampled from two to 10 times.The resampled individuals also showed highly heterogeneous permanence periods, ranging from a few days to about a few months.
Individual membership proportions to the reference wolf population (qw) estimated from the Bayesian assignment procedures, according to the selected q-thresholds (Caniglia et al.S2).
When the results obtained from the assignment procedures were integrated with the uniparental (mtDNA CR, four Y-linked STRs) and coding (K-locus) data, 22 wolves were reclassified as introgressed individuals since 16 males (corresponding to 9% of the wolf males) showed dog Yhaplotypes and 6 (1.5%; 3 females and 3 males) showed the melanistic 3bp deletion.Additionally, among the 58 introgressed individuals, 3 males also showed a dog Y-haplotype and one female (1.7%) showed the melanistic 3bp deletion.Among the 60 recent wolf-dog hybrids, 8 males (corresponding to 28% of the wolf-dog hybrid males) also showed a dog Y-haplotype and 6 individuals (10%; 3 females, 2 males and 1 with undetermined gender) also the melanistic 3bp deletion.All the individuals identified as wolves, recent hybrids or introgressed animals showed mtDNA haplotypes typical of the Italian wolf population (Montana et al 2017).

Table S2 -
Description of the genetic variability obtained analysing the 12-STR genotypes identified from both non-invasively and invasively collected samples.